so i had to edit cuz enough new question
soo i think i properly prepared my data to be trained by the svm but i keep getting a fatal error
> # A fatal error has been detected by the Java Runtime Environment:
> #
> # SIGSEGV (0xb) at pc=0x00007f68840000f8, pid=13617, tid=0x00007f688b290700
pc=0x0000000000000000, pid=10990,
> tid=0x00007f110a447700
> #
> # JRE version: Java(TM) SE Runtime Environment (8.0_121-b13) (build (build
> 1.8.0_121-b13)
> # Java VM: Java HotSpot(TM) 64-Bit Server VM (25.121-b13 mixed mode mode
> linux-amd64 compressed oops)
> # Problematic frame:
> # C 0x00007f68840000f8
0x0000000000000000
> #
> # Failed to write core dump. Core dumps have been disabled. To enable enable
> core dumping, try "ulimit -c -c
> unlimited" before starting Java again
> #
> # An error report file with more information is saved as:
# /home/tomna/NetBeansProjects/main/hs_err_pid13617.log
> # /home/tomna/Desktop/NB/MainClass/hs_err_pid10990.log
> #
> # If you would like to submit a bug report, please visit:
> # http://bugreport.java.com/bugreport/crash.jsp
> # The crash happened outside the Java Virtual Machine in native code.
> # See problematic frame for where to report the bug.
> #
/home/tomna/.cache/netbeans/8.1/executor-snippets/run.xml:53: Java returned: 134
i am trying to train my SVM and this error appeared no idea why i am guessing to many features to be extracted since my image sizes are 309*163 PIXELS here is my code
protected static final String PATH_POSITIVE = "/home/tomna/NetBeansProjects/main/src/main/PostiveTrain";
protected static final String PATH_NEGATIVE = "/home/tomna/NetBeansProjects/main/src/main/NegativeTrain";
protected static final String XML = "/home/tomna/NetBeansProjects/main/src/main/data.xml";
protected static final String FILE_TEST = "/home/tomna/NetBeansProjects/main/src/main/Dadcar3.png";
private static Mat trainingImages = new Mat();
private static Mat trainingLabels = new Mat(0,1,CvType.CV_32FC1);
Mat();
private static Mat trainingData = new Mat();
private static Mat classes = new Mat();
private static SVM clasificador = SVM.create();
public static MatOfPoint loacation = new MatOfPoint();
public static MatOfDouble descriptors = new MatOfDouble();
// private static HOGDescriptor hog = new HOGDescriptor(_winSize, _blockSize, _blockStride, _cellSize, CV_32FC1);
MatOfFloat v_descriptors = new MatOfFloat();
public static TermCriteria S = new TermCriteria(TermCriteria.EPS+TermCriteria.MAX_ITER,1000,1e-6);
void run() throws IOException {
trainPositives();
trainNegatives();
trains();
test();
}
public static Mat getMat(String path) {
Mat img = new Mat();
Mat convert_to_gray = Imgcodecs.imread(path, Imgcodecs.CV_LOAD_IMAGE_GRAYSCALE);
convert_to_gray.convertTo(img, CvType.CV_32FC1);
return img;
}
public static void test() {
Mat input = Imgcodecs.imread(new File(FILE_TEST).getAbsolutePath());
clasificador.save(new File(XML).getAbsolutePath());
System.out.println(clasificador);
Mat output = new Mat();
input.convertTo(output, CV_32FC1);
output = output.reshape(64, 64);
System.out.println(output);
System.out.println(clasificador.predict(output));
}
TermCriteria(TermCriteria.MAX_ITER+TermCriteria.EPS, 1000, 1e-6);
public static void trains() {
trainingImages.copyTo(trainingData);
System.out.println("Training...");
v_descriptors.copyTo(trainingData);
trainingData.convertTo(trainingData, CvType.CV_32FC1);
CvType.CV_32F);
trainingLabels.copyTo(classes);
classes.convertTo(classes,CvType.CV_32S);
clasificador.setType(SVM.C_SVC);
clasificador.setKernel(SVM.LINEAR);
clasificador.setType(SVM.C_SVC);
// clasificador.setDegree(0.5);
// clasificador.setGamma(1);
// clasificador.setCoef0(0);
// clasificador.setC(7);
// clasificador.setNu(0.5);
// clasificador.setP(0.0);
clasificador.setTermCriteria(S);
clasificador.train(trainingData,Ml.ROW_SAMPLE,classes);
// System.out.print("Trained and Ready to use");
clasificador.train(trainingData, ROW_SAMPLE, classes);
clasificador.save(XML);
}
public static void trainPositives() {
MatOfFloat descriptorsValues = new MatOfFloat();
for (File file : new File(PATH_POSITIVE).listFiles()) {
Mat img = getMat(file.getAbsolutePath());
trainingImages.push_back(img.reshape(1, HOGDescriptor d = new HOGDescriptor(new Size(32, 16), new Size(8, 8), new Size(4, 4), new Size(4, 4), 9);
d.compute(img, descriptorsValues);
Mat labelsMat = new Mat(1, 1, CvType.CV_32SC1, new Scalar(1));
v_descriptors.push_back(descriptorsValues.reshape(1, 1));
trainingLabels.push_back(Mat.ones(new Size(1, 1), CvType.CV_32S));
trainingLabels.push_back(labelsMat);
}
//
System.out.println(v_descriptors);
System.out.println(trainingLabels);
}
public static void trainNegatives() {
MatOfFloat descriptorsValues2 = new MatOfFloat();
for (File file : new File(PATH_NEGATIVE).listFiles()) {
Mat img = getMat(file.getAbsolutePath());
trainingImages.push_back(img.reshape(1, HOGDescriptor d = new HOGDescriptor(new Size(32, 16), new Size(8, 8), new Size(4, 4), new Size(4, 4), 9);
d.compute(img, descriptorsValues2);
Mat labelsMat = new Mat(1, 1, CvType.CV_32SC1, new Scalar(-1));
v_descriptors.push_back(descriptorsValues2.reshape(1, 1));
trainingLabels.push_back(Mat.zeros(new Size(1, 1), CvType.CV_32S));
trainingLabels.push_back(labelsMat);
}
System.out.println(v_descriptors);
System.out.println(trainingLabels);
}
and another thing i want to train my SVM on HOG feature so
i am not sure where to apply such lines
hog.compute (parameters, parameters );
hog.setSVMDetector( );
Kindly help me resolve the above error.
